RESEARCH
PUBLICATIONS
2022
Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach
Makrina Karaglani, Maria Panagopoulou, Ismini Baltsavia, Paraskevi Apalaki, Theodosis Theodosiou, Ioannis Iliopoulos, Ioannis Tsamardinos, Ekaterini Chatzaki
International Journal of Molecular Sciences
Learning biologically-interpretable latent representations for gene expression data
Ioulia Karagiannaki, Krystallia Gourlia, Vincenzo Lagani, Yannis Pantazis & Ioannis Tsamardinos
Springer, Published: 29 April 2022
2021
Prediction of outcome in patients with non-small cell lung cancer treated with second line PD-1/PDL-1 inhibitors based on clinical parameters: Results from a prospective, single institution study
Rounis Konstantinos, Makrakis Dimitrios, Papadaki Chara, Monastirioti Alexia, Vamvakas Lambros, Kalbakis Konstantinos, Gourlia Krystallia, Xanthopoulos Iordanis, Tsamardinos Ioannis, Mavroudis Dimitrios
Public Library of Science San Francisco, CA USA
PROTEUS: Predictive Explanation of Anomalies
Nikos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides
37th IEEE International Conference on Data Engineering (ICDE) 2021 (to appear)
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment
J. Marcos-Zambrano, K. Karaduzovic-Hadziabdic, T. Turukalo, P. Przymus, V. Trajkovik, O. Aasmets, M. Berland, G. Gruca, J. Hasic, K. Hron, T. Klammsteiner, M. Kolev, L. Lanthi, M. Lopez, V. Moreno, I. Naskinova, E. Org, I. Paciência, G. Papoutsoglou, R. Shigdel, B. Stres, B. Vilne, M. Yousef, E. Zdravevski, I. Tsamardinos, E. Carrillo de Santa Pau, M. Claesson, I. Moreno-Indias, and J. Truu
Frontiers in Microbiology, vol. 12, 2021
Extending greedy feature selection algorithms to multiple solutions
Tsamardinos Ioannis, Borboudakis Georgios
Springer, 35, pages1393–1434 (2021)
Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning
Panagopoulou Maria, Karaglani Makrina, Manolopoulos Vangelis, G Iliopoulos, Ioannis Tsamardinos, Ioannis Chatzaki Ekaterini
Multidisciplinary Digital Publishing Institute
An AutoML application to forecasting bank failures
Agrapetidou Anna, Charonyktakis Paulos, Gogas Periklis, Papadimitriou Theophilos, Tsamardinos Ioannis
Taylor & Francis
Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets
Georgios Papoutsoglou, Makrina Karaglani, Vincenzo Lagani, Naomi Thomson, Oluf Dimitri Røe, Ioannis Tsamardinos & Ekaterini Chatzaki
Nature Publishing Group
2020
Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data
Karagiannaki I., Pantazis Y., Chatzaki E., Tsamardinos I. (2020). In: Appice A., Tsoumakas G., Manolopoulos Y., Matwin S. (eds) Discovery Science. DS 2020.
Lecture Notes in Computer Science, vol 12323. Springer, Cham.
Accurate blood-based diagnostic biosignatures for Alzheimer’s disease via Automated machine learning
Makrina Karaglani, Krystallia Gourlia, Ioannis Tsamardinos, Ekaterini Chatzaki
Journal of Clinical Medicine, 2020, 9(9)
Tuning Causal Discovery Algorithms
Konstantina Biza, Ioannis Tsamardinos, Sofia Triantafillou
Proceedings of the Tenth International Conference on Probabilistic Graphical Models, in PMLR, 2020
Putting the Human Back in the AutoML Loop
Iordanis Xanthopoulos, Ioannis Tsamardinos, Vassilis Christophides, Eric Simon, Alejandro Salinger
EDBT/ICDT Workshops 2020
An Automated Machine Learning architecture for the accelerated prediction of Metal-Organic Frameworks performance in energy and environmental applications
Ioannis Tsamardinos, George Fanourgakis, Elissavet Greasidou, Emmanuel Klontzas, Konstantinos Gkagka, George E.Froudakis
Microporous and Mesoporous Materials 300, 2020
Learning Pathway Dynamics from Single‐Cell Proteomic Data: A Comparative Study
Kleio‐Maria Verrou , Ioannis Tsamardinos, Georgios Papoutsoglou
Cytometry Part A, 97 (3), 2020, Special Issue: Machine Learning for Single Cell Data
An AutoML Application to Forecasting Bank Failures
Anna Agrapetidou, Paulos Charonyktakis, Periklis Gogas, Theophilos Papadimitriou, Ioannis Tsamardinos
Applied Economics Letters, 2020, DOI: 10.1080/13504851.2020.1725230
Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning
Karstoft KI, Tsamardinos I, Eskelund K, Andersen SB, Nissen LR
JMIR Med Inform 2020;8(7):e17119
Translating vitamin D transcriptomics to clinical evidence: analysis of data in asthma and chronic obstructive pulmonary disease, followed by clinical data meta-analysis
Niki Malliaraki, Kleanthi Lakiotaki, Rodoula Vamvoukaki, George Notas, Ioannis Tsamardinos, Marilena Kampa, Elias Castanas
Journal of Steroid Biochemistry and Molecular Biology 197, 2020